Joint embedding learning and sparse regression: A framework for unsupervised feature selection

IEEE Transactions on Cybernetics

Volumn 44, Issue 6, 2014, Pages 793-804

  Hou, Chenping ^a   Nie, Feiping ^b   Li, Xuelong ^c   Yi, Dongyun ^a   Wu, Yi ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b The University of Texas at Arlington   (United States)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Embedding learning; feature selection; pattern recognition; sparse regression

Indexed keywords

APPROXIMATION ALGORITHMS; BIOINFORMATICS; FEATURE EXTRACTION; OPTIMIZATION; PATTERN RECOGNITION;

EFFECTIVE ALGORITHMS; EMBEDDING LEARNING; FEATURE SELECTION METHODS; LOCAL LINEAR APPROXIMATION; OPTIMIZATION PROBLEMS; PARAMETER DETERMINATION; SPARSE REGRESSION; UNSUPERVISED FEATURE SELECTION;

REGRESSION ANALYSIS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; PROCEDURES; REGRESSION ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS;

EID: 84901250680     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2013.2272642     Document Type: Article

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